{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/PacktPublishing/Modern-Computer-Vision-with-PyTorch/blob/master/Chapter10/Human_pose_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yuYzbvaxcOAc",
    "outputId": "0010da43-29af-46d6-cf5e-7f126142bdc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n",
      "Cloning into 'detectron2'...\n",
      "remote: Enumerating objects: 15725, done.\u001b[K\n",
      "remote: Counting objects: 100% (448/448), done.\u001b[K\n",
      "remote: Compressing objects: 100% (336/336), done.\u001b[K\n",
      "remote: Total 15725 (delta 201), reused 302 (delta 103), pack-reused 15277\u001b[K\n",
      "Receiving objects: 100% (15725/15725), 6.51 MiB | 21.17 MiB/s, done.\n",
      "Resolving deltas: 100% (11313/11313), done.\n",
      "/content/detectron2\n",
      "\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements.txt'\u001b[0m\u001b[31m\n",
      "\u001b[0mrunning install\n",
      "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n",
      "!!\n",
      "\n",
      "        ********************************************************************************\n",
      "        Please avoid running ``setup.py`` directly.\n",
      "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
      "        other standards-based tools.\n",
      "\n",
      "        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n",
      "        ********************************************************************************\n",
      "\n",
      "!!\n",
      "  self.initialize_options()\n",
      "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: EasyInstallDeprecationWarning: easy_install command is deprecated.\n",
      "!!\n",
      "\n",
      "        ********************************************************************************\n",
      "        Please avoid running ``setup.py`` and ``easy_install``.\n",
      "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
      "        other standards-based tools.\n",
      "\n",
      "        See https://github.com/pypa/setuptools/issues/917 for details.\n",
      "        ********************************************************************************\n",
      "\n",
      "!!\n",
      "  self.initialize_options()\n",
      "running bdist_egg\n",
      "running egg_info\n",
      "creating detectron2.egg-info\n",
      "writing detectron2.egg-info/PKG-INFO\n",
      "writing dependency_links to detectron2.egg-info/dependency_links.txt\n",
      "writing requirements to detectron2.egg-info/requires.txt\n",
      "writing top-level names to detectron2.egg-info/top_level.txt\n",
      "writing manifest file 'detectron2.egg-info/SOURCES.txt'\n",
      "/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:499: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n",
      "  warnings.warn(msg.format('we could not find ninja.'))\n",
      "reading manifest file 'detectron2.egg-info/SOURCES.txt'\n",
      "adding license file 'LICENSE'\n",
      "writing manifest file 'detectron2.egg-info/SOURCES.txt'\n",
      "installing library code to build/bdist.linux-x86_64/egg\n",
      "running install_lib\n",
      "running build_py\n",
      "creating build\n",
      "creating build/lib.linux-x86_64-cpython-310\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2\n",
      "copying detectron2/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2\n",
      "creating build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/visualize_data.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/train_net.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/analyze_model.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/plain_train_net.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/benchmark.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/lightning_train_net.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/visualize_json_results.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/lazyconfig_train_net.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/__init__.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "copying tools/convert-torchvision-to-d2.py -> build/lib.linux-x86_64-cpython-310/tools\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/solver\n",
      "copying detectron2/solver/build.py -> build/lib.linux-x86_64-cpython-310/detectron2/solver\n",
      "copying detectron2/solver/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/solver\n",
      "copying detectron2/solver/lr_scheduler.py -> build/lib.linux-x86_64-cpython-310/detectron2/solver\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/base_tracker.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/utils.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/hungarian_tracker.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/bbox_iou_tracker.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "copying detectron2/tracking/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/tracking\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/engine\n",
      "copying detectron2/engine/launch.py -> build/lib.linux-x86_64-cpython-310/detectron2/engine\n",
      "copying detectron2/engine/hooks.py -> build/lib.linux-x86_64-cpython-310/detectron2/engine\n",
      "copying detectron2/engine/train_loop.py -> build/lib.linux-x86_64-cpython-310/detectron2/engine\n",
      "copying detectron2/engine/defaults.py -> build/lib.linux-x86_64-cpython-310/detectron2/engine\n",
      "copying detectron2/engine/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/engine\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/checkpoint\n",
      "copying detectron2/checkpoint/detection_checkpoint.py -> build/lib.linux-x86_64-cpython-310/detectron2/checkpoint\n",
      "copying detectron2/checkpoint/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/checkpoint\n",
      "copying detectron2/checkpoint/catalog.py -> build/lib.linux-x86_64-cpython-310/detectron2/checkpoint\n",
      "copying detectron2/checkpoint/c2_model_loading.py -> build/lib.linux-x86_64-cpython-310/detectron2/checkpoint\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/projects\n",
      "copying detectron2/projects/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/collect_env.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/analysis.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/events.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/video_visualizer.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/registry.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/env.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/colormap.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/tracing.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/comm.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/file_io.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/memory.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/logger.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/serialize.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/testing.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/visualizer.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "copying detectron2/utils/develop.py -> build/lib.linux-x86_64-cpython-310/detectron2/utils\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/torchscript.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/caffe2_modeling.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/flatten.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/caffe2_inference.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/caffe2_export.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/c10.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/torchscript_patch.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/api.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/shared.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/caffe2_patch.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "copying detectron2/export/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/export\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "copying detectron2/config/lazy.py -> build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "copying detectron2/config/instantiate.py -> build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "copying detectron2/config/config.py -> build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "copying detectron2/config/compat.py -> build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "copying detectron2/config/defaults.py -> build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "copying detectron2/config/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/config\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/panoptic_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/rotated_coco_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/lvis_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/pascal_voc_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/coco_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/fast_eval_api.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/testing.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/sem_seg_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/cityscapes_evaluation.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "copying detectron2/evaluation/evaluator.py -> build/lib.linux-x86_64-cpython-310/detectron2/evaluation\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/masks.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/instances.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/rotated_boxes.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/keypoints.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/image_list.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "copying detectron2/structures/boxes.py -> build/lib.linux-x86_64-cpython-310/detectron2/structures\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo\n",
      "copying detectron2/model_zoo/model_zoo.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo\n",
      "copying detectron2/model_zoo/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/deform_conv.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/blocks.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/aspp.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/losses.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/shape_spec.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/nms.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/rotated_boxes.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/batch_norm.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/roi_align_rotated.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/roi_align.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/mask_ops.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "copying detectron2/layers/wrappers.py -> build/lib.linux-x86_64-cpython-310/detectron2/layers\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/detection_utils.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/benchmark.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/build.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/common.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/dataset_mapper.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "copying detectron2/data/catalog.py -> build/lib.linux-x86_64-cpython-310/detectron2/data\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/anchor_generator.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/matcher.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/poolers.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/sampling.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/box_regression.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/mmdet_wrapper.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/postprocessing.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "copying detectron2/modeling/test_time_augmentation.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/data/transforms\n",
      "copying detectron2/data/transforms/augmentation.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/transforms\n",
      "copying detectron2/data/transforms/augmentation_impl.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/transforms\n",
      "copying detectron2/data/transforms/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/transforms\n",
      "copying detectron2/data/transforms/transform.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/transforms\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/data/samplers\n",
      "copying detectron2/data/samplers/distributed_sampler.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/samplers\n",
      "copying detectron2/data/samplers/grouped_batch_sampler.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/samplers\n",
      "copying detectron2/data/samplers/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/samplers\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/pascal_voc.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/lvis_v1_category_image_count.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/cityscapes.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/lvis.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/cityscapes_panoptic.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/lvis_v1_categories.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/coco.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/lvis_v0_5_categories.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/register_coco.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/builtin.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/builtin_meta.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "copying detectron2/data/datasets/coco_panoptic.py -> build/lib.linux-x86_64-cpython-310/detectron2/data/datasets\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/backbone.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/vit.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/regnet.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/utils.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/swin.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/build.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/mvit.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/resnet.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "copying detectron2/modeling/backbone/fpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/backbone\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/keypoint_head.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/box_head.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/rotated_fast_rcnn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/cascade_rcnn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/fast_rcnn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/roi_heads.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/mask_head.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "copying detectron2/modeling/roi_heads/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/roi_heads\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/modeling/proposal_generator\n",
      "copying detectron2/modeling/proposal_generator/rrpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/proposal_generator\n",
      "copying detectron2/modeling/proposal_generator/rpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/proposal_generator\n",
      "copying detectron2/modeling/proposal_generator/build.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/proposal_generator\n",
      "copying detectron2/modeling/proposal_generator/proposal_utils.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/proposal_generator\n",
      "copying detectron2/modeling/proposal_generator/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/proposal_generator\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/panoptic_fpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/dense_detector.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/retinanet.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/fcos.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/rcnn.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/build.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/semantic_seg.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "copying detectron2/modeling/meta_arch/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/modeling/meta_arch\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/point_features.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/config.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/roi_heads.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/semantic_seg.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/color_augmentation.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/mask_head.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "copying projects/PointRend/point_rend/point_head.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/point_rend\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/config.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/build_solver.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/loss.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/semantic_seg.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/resnet.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "copying projects/DeepLab/deeplab/lr_scheduler.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "copying projects/Panoptic-DeepLab/panoptic_deeplab/config.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "copying projects/Panoptic-DeepLab/panoptic_deeplab/post_processing.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "copying projects/Panoptic-DeepLab/panoptic_deeplab/panoptic_seg.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "copying projects/Panoptic-DeepLab/panoptic_deeplab/target_generator.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "copying projects/Panoptic-DeepLab/panoptic_deeplab/__init__.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "copying projects/Panoptic-DeepLab/panoptic_deeplab/dataset_mapper.py -> build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs\n",
      "copying detectron2/model_zoo/configs/Base-RCNN-FPN.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs\n",
      "copying detectron2/model_zoo/configs/Base-RCNN-C4.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs\n",
      "copying detectron2/model_zoo/configs/Base-RCNN-DilatedC5.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs\n",
      "copying detectron2/model_zoo/configs/Base-RetinaNet.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/PascalVOC-Detection\n",
      "copying detectron2/model_zoo/configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/PascalVOC-Detection\n",
      "copying detectron2/model_zoo/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/PascalVOC-Detection\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_FPN_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_DC5_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/retinanet_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_FPN_pred_boxes_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/panoptic_fpn_R_50_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/rpn_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/semantic_R_50_FPN_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/semantic_R_50_FPN_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/rpn_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/panoptic_fpn_R_50_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/semantic_R_50_FPN_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "copying detectron2/model_zoo/configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/quick_schedules\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/LVISv1-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying detectron2/model_zoo/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying detectron2/model_zoo/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying detectron2/model_zoo/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/semantic_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/rpn_R_50_C4_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Cityscapes\n",
      "copying detectron2/model_zoo/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Cityscapes\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "copying detectron2/model_zoo/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "copying detectron2/model_zoo/configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "copying detectron2/model_zoo/configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "copying detectron2/model_zoo/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "copying detectron2/model_zoo/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "copying detectron2/model_zoo/configs/new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/new_baselines\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common\n",
      "copying detectron2/model_zoo/configs/common/optim.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common\n",
      "copying detectron2/model_zoo/configs/common/train.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common\n",
      "copying detectron2/model_zoo/configs/common/coco_schedule.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/mask_rcnn_vitdet.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/panoptic_fpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/mask_rcnn_c4.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/retinanet.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/cascade_rcnn.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/fcos.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/mask_rcnn_fpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "copying detectron2/model_zoo/configs/common/models/keypoint_rcnn_fpn.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models\n",
      "creating build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data\n",
      "copying detectron2/model_zoo/configs/common/data/constants.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data\n",
      "copying detectron2/model_zoo/configs/common/data/coco_keypoint.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data\n",
      "copying detectron2/model_zoo/configs/common/data/coco.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data\n",
      "copying detectron2/model_zoo/configs/common/data/coco_panoptic_separated.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data\n",
      "copying detectron2/model_zoo/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/Misc/torchvision_imagenet_R_50.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/fcos_R_50_FPN_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-Detection/retinanet_R_50_FPN_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying detectron2/model_zoo/configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py -> build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "running build_ext\n",
      "/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:418: UserWarning: The detected CUDA version (12.2) has a minor version mismatch with the version that was used to compile PyTorch (12.1). Most likely this shouldn't be a problem.\n",
      "  warnings.warn(CUDA_MISMATCH_WARN.format(cuda_str_version, torch.version.cuda))\n",
      "/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:428: UserWarning: There are no x86_64-linux-gnu-g++ version bounds defined for CUDA version 12.2\n",
      "  warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}')\n",
      "building 'detectron2._C' extension\n",
      "creating build/temp.linux-x86_64-cpython-310\n",
      "creating build/temp.linux-x86_64-cpython-310/content\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/ROIAlignRotated\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/box_iou_rotated\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/cocoeval\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/deformable\n",
      "creating build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/nms_rotated\n",
      "x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.cpp -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17\n",
      "/usr/local/lib/python3.10/dist-packages/torch/utils/cpp_extension.py:1967: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \n",
      "If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].\n",
      "  warnings.warn(\n",
      "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.cu -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++17\n",
      "x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.cpp -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17\n",
      "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++17\n",
      "x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/cocoeval/cocoeval.cpp -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/cocoeval/cocoeval.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17\n",
      "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/cuda_version.cu -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/cuda_version.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++17\n",
      "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/deformable/deform_conv_cuda.cu -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/deformable/deform_conv_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++17\n",
      "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.cu -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++17\n",
      "x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17\n",
      "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.cu -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++17\n",
      "x86_64-linux-gnu-gcc -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -DWITH_CUDA -I/content/detectron2/detectron2/layers/csrc -I/usr/local/lib/python3.10/dist-packages/torch/include -I/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.10/dist-packages/torch/include/TH -I/usr/local/lib/python3.10/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.10 -c /content/detectron2/detectron2/layers/csrc/vision.cpp -o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/vision.o -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\\\"_gcc\\\" -DPYBIND11_STDLIB=\\\"_libstdcpp\\\" -DPYBIND11_BUILD_ABI=\\\"_cxxabi1011\\\" -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17\n",
      "In file included from \u001b[01m\u001b[K/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/Exceptions.h:12\u001b[m\u001b[K,\n",
      "                 from \u001b[01m\u001b[K/usr/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include/torch/python.h:11\u001b[m\u001b[K,\n",
      "                 from \u001b[01m\u001b[K/usr/local/lib/python3.10/dist-packages/torch/include/torch/extension.h:9\u001b[m\u001b[K,\n",
      "                 from \u001b[01m\u001b[K/content/detectron2/detectron2/layers/csrc/vision.cpp:3\u001b[m\u001b[K:\n",
      "/usr/local/lib/python3.10/dist-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘\u001b[01m\u001b[Kclass pybind11::class_<detectron2::COCOeval::InstanceAnnotation>\u001b[m\u001b[K’:\n",
      "\u001b[01m\u001b[K/content/detectron2/detectron2/layers/csrc/vision.cpp:105:73:\u001b[m\u001b[K   required from here\n",
      "\u001b[01m\u001b[K/usr/local/lib/python3.10/dist-packages/torch/include/pybind11/pybind11.h:1555:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kpybind11::class_<detectron2::COCOeval::InstanceAnnotation>\u001b[m\u001b[K’ declared with greater visibility than its base ‘\u001b[01m\u001b[Kpybind11::detail::generic_type\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wattributes\u0007-Wattributes\u001b]8;;\u0007\u001b[m\u001b[K]\n",
      " 1555 | class \u001b[01;35m\u001b[Kclass_\u001b[m\u001b[K : public detail::generic_type {\n",
      "      |       \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
      "/usr/local/lib/python3.10/dist-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘\u001b[01m\u001b[Kclass pybind11::class_<detectron2::COCOeval::ImageEvaluation>\u001b[m\u001b[K’:\n",
      "\u001b[01m\u001b[K/content/detectron2/detectron2/layers/csrc/vision.cpp:107:67:\u001b[m\u001b[K   required from here\n",
      "\u001b[01m\u001b[K/usr/local/lib/python3.10/dist-packages/torch/include/pybind11/pybind11.h:1555:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kpybind11::class_<detectron2::COCOeval::ImageEvaluation>\u001b[m\u001b[K’ declared with greater visibility than its base ‘\u001b[01m\u001b[Kpybind11::detail::generic_type\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wattributes\u0007-Wattributes\u001b]8;;\u0007\u001b[m\u001b[K]\n",
      "x86_64-linux-gnu-g++ -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cpu.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated_cuda.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cpu.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/cocoeval/cocoeval.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/cuda_version.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/deformable/deform_conv_cuda.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/deformable/deform_conv_cuda_kernel.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/nms_rotated/nms_rotated_cuda.o build/temp.linux-x86_64-cpython-310/content/detectron2/detectron2/layers/csrc/vision.o -L/usr/local/lib/python3.10/dist-packages/torch/lib -L/usr/local/cuda/lib64 -L/usr/lib/x86_64-linux-gnu -lc10 -ltorch -ltorch_cpu -ltorch_python -lcudart -lc10_cuda -ltorch_cuda -o build/lib.linux-x86_64-cpython-310/detectron2/_C.cpython-310-x86_64-linux-gnu.so\n",
      "creating build/bdist.linux-x86_64\n",
      "creating build/bdist.linux-x86_64/egg\n",
      "creating build/bdist.linux-x86_64/egg/detectron2\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/solver\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/solver/build.py -> build/bdist.linux-x86_64/egg/detectron2/solver\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/solver/__init__.py -> build/bdist.linux-x86_64/egg/detectron2/solver\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/solver/lr_scheduler.py -> build/bdist.linux-x86_64/egg/detectron2/solver\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/base_tracker.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/utils.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/hungarian_tracker.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/bbox_iou_tracker.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/tracking/__init__.py -> build/bdist.linux-x86_64/egg/detectron2/tracking\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/engine\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/engine/launch.py -> build/bdist.linux-x86_64/egg/detectron2/engine\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/engine/hooks.py -> build/bdist.linux-x86_64/egg/detectron2/engine\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/engine/train_loop.py -> build/bdist.linux-x86_64/egg/detectron2/engine\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/engine/defaults.py -> build/bdist.linux-x86_64/egg/detectron2/engine\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/engine/__init__.py -> build/bdist.linux-x86_64/egg/detectron2/engine\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/checkpoint\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/checkpoint/detection_checkpoint.py -> build/bdist.linux-x86_64/egg/detectron2/checkpoint\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/checkpoint/__init__.py -> build/bdist.linux-x86_64/egg/detectron2/checkpoint\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/checkpoint/catalog.py -> build/bdist.linux-x86_64/egg/detectron2/checkpoint\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/checkpoint/c2_model_loading.py -> build/bdist.linux-x86_64/egg/detectron2/checkpoint\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/projects\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/config.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/build_solver.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/loss.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/semantic_seg.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/__init__.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/resnet.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/deeplab/lr_scheduler.py -> build/bdist.linux-x86_64/egg/detectron2/projects/deeplab\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/projects/panoptic_deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab/config.py -> build/bdist.linux-x86_64/egg/detectron2/projects/panoptic_deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab/post_processing.py -> build/bdist.linux-x86_64/egg/detectron2/projects/panoptic_deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab/panoptic_seg.py -> build/bdist.linux-x86_64/egg/detectron2/projects/panoptic_deeplab\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/projects/panoptic_deeplab/target_generator.py -> build/bdist.linux-x86_64/egg/detectron2/projects/panoptic_deeplab\n",
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      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv1-InstanceSegmentation\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Detectron1-Comparisons\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Base-RCNN-FPN.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/optim.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/mask_rcnn_vitdet.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/panoptic_fpn.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/mask_rcnn_c4.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/retinanet.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/cascade_rcnn.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/fcos.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/mask_rcnn_fpn.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/models/keypoint_rcnn_fpn.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/models\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/train.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/data\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data/constants.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/data\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data/coco_keypoint.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/data\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data/coco.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/data\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/data/coco_panoptic_separated.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common/data\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/common/coco_schedule.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/common\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_1x_cls_agnostic.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/semantic_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/torchvision_imagenet_R_50.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Misc\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/fcos_R_50_FPN_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/rpn_R_50_C4_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/retinanet_R_50_FPN_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Detection\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-InstanceSegmentation\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/LVISv0.5-InstanceSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Base-RCNN-C4.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Cityscapes\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Cityscapes/mask_rcnn_R_50_FPN.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/Cityscapes\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Base-RCNN-DilatedC5.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.py -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-PanopticSegmentation\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/Base-RetinaNet.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs\n",
      "creating build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Keypoints\n",
      "copying build/lib.linux-x86_64-cpython-310/detectron2/model_zoo/configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml -> build/bdist.linux-x86_64/egg/detectron2/model_zoo/configs/COCO-Keypoints\n",
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      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/backbone/build.py to build.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/backbone/mvit.py to mvit.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/backbone/__init__.py to __init__.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/backbone/resnet.py to resnet.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/backbone/fpn.py to fpn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/mmdet_wrapper.py to mmdet_wrapper.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/keypoint_head.py to keypoint_head.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/box_head.py to box_head.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/rotated_fast_rcnn.py to rotated_fast_rcnn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/cascade_rcnn.py to cascade_rcnn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/fast_rcnn.py to fast_rcnn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/roi_heads.py to roi_heads.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/mask_head.py to mask_head.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/roi_heads/__init__.py to __init__.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/proposal_generator/rrpn.py to rrpn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/proposal_generator/rpn.py to rpn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/proposal_generator/build.py to build.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/proposal_generator/proposal_utils.py to proposal_utils.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/proposal_generator/__init__.py to __init__.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/panoptic_fpn.py to panoptic_fpn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/dense_detector.py to dense_detector.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/retinanet.py to retinanet.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/fcos.py to fcos.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/rcnn.py to rcnn.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/build.py to build.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/semantic_seg.py to semantic_seg.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/meta_arch/__init__.py to __init__.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/__init__.py to __init__.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/postprocessing.py to postprocessing.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/modeling/test_time_augmentation.py to test_time_augmentation.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/visualize_data.py to visualize_data.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/train_net.py to train_net.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/analyze_model.py to analyze_model.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/plain_train_net.py to plain_train_net.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/benchmark.py to benchmark.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/lightning_train_net.py to lightning_train_net.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/visualize_json_results.py to visualize_json_results.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/lazyconfig_train_net.py to lazyconfig_train_net.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/__init__.py to __init__.cpython-310.pyc\n",
      "byte-compiling build/bdist.linux-x86_64/egg/tools/convert-torchvision-to-d2.py to convert-torchvision-to-d2.cpython-310.pyc\n",
      "creating stub loader for detectron2/_C.cpython-310-x86_64-linux-gnu.so\n",
      "byte-compiling build/bdist.linux-x86_64/egg/detectron2/_C.py to _C.cpython-310.pyc\n",
      "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
      "copying detectron2.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
      "copying detectron2.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
      "copying detectron2.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
      "copying detectron2.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
      "copying detectron2.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
      "writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "detectron2.__pycache__._C.cpython-310: module references __file__\n",
      "detectron2.config.__pycache__.lazy.cpython-310: module references __file__\n",
      "detectron2.config.__pycache__.lazy.cpython-310: module MAY be using inspect.stack\n",
      "detectron2.projects.__pycache__.__init__.cpython-310: module references __file__\n",
      "detectron2.utils.__pycache__.collect_env.cpython-310: module references __file__\n",
      "creating dist\n",
      "creating 'dist/detectron2-0.6-py3.10-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
      "Processing detectron2-0.6-py3.10-linux-x86_64.egg\n",
      "creating /usr/local/lib/python3.10/dist-packages/detectron2-0.6-py3.10-linux-x86_64.egg\n",
      "Extracting detectron2-0.6-py3.10-linux-x86_64.egg to /usr/local/lib/python3.10/dist-packages\n",
      "Adding detectron2 0.6 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/detectron2-0.6-py3.10-linux-x86_64.egg\n",
      "Processing dependencies for detectron2==0.6\n",
      "Searching for black\n",
      "Reading https://pypi.org/simple/black/\n",
      "Downloading https://files.pythonhosted.org/packages/e0/7d/7f8df0fdbbbefc4362d3eca6b69b7a8a4249a8a88dabc00a207d31fddcd7/black-24.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=eaea3008c281f1038edb473c1aa8ed8143a5535ff18f978a318f10302b254063\n",
      "Best match: black 24.4.2\n",
      "Processing black-24.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n",
      "Installing black-24.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding black 24.4.2 to easy-install.pth file\n",
      "Installing black script to /usr/local/bin\n",
      "Installing blackd script to /usr/local/bin\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/black-24.4.2-py3.10-linux-x86_64.egg\n",
      "Searching for hydra-core>=1.1\n",
      "Reading https://pypi.org/simple/hydra-core/\n",
      "Downloading https://files.pythonhosted.org/packages/c6/50/e0edd38dcd63fb26a8547f13d28f7a008bc4a3fd4eb4ff030673f22ad41a/hydra_core-1.3.2-py3-none-any.whl#sha256=fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b\n",
      "Best match: hydra-core 1.3.2\n",
      "Processing hydra_core-1.3.2-py3-none-any.whl\n",
      "Installing hydra_core-1.3.2-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding hydra-core 1.3.2 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/hydra_core-1.3.2-py3.10.egg\n",
      "Searching for omegaconf<2.4,>=2.1\n",
      "Reading https://pypi.org/simple/omegaconf/\n",
      "Downloading https://files.pythonhosted.org/packages/e3/94/1843518e420fa3ed6919835845df698c7e27e183cb997394e4a670973a65/omegaconf-2.3.0-py3-none-any.whl#sha256=7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b\n",
      "Best match: omegaconf 2.3.0\n",
      "Processing omegaconf-2.3.0-py3-none-any.whl\n",
      "Installing omegaconf-2.3.0-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding omegaconf 2.3.0 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/omegaconf-2.3.0-py3.10.egg\n",
      "Searching for iopath<0.1.10,>=0.1.7\n",
      "Reading https://pypi.org/simple/iopath/\n",
      "Downloading https://files.pythonhosted.org/packages/af/20/65dd9bd25a1eb7fa35b5ae38d289126af065f8a0c1f6a90564f4bff0f89d/iopath-0.1.9-py3-none-any.whl#sha256=9058ac24f0328decdf8dbe209b33074b8702a3c4d9ba2f7801d46cb61a880b6e\n",
      "Best match: iopath 0.1.9\n",
      "Processing iopath-0.1.9-py3-none-any.whl\n",
      "Installing iopath-0.1.9-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding iopath 0.1.9 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/iopath-0.1.9-py3.10.egg\n",
      "Searching for fvcore<0.1.6,>=0.1.5\n",
      "Reading https://pypi.org/simple/fvcore/\n",
      "Downloading https://files.pythonhosted.org/packages/a5/93/d056a9c4efc6c79ba7b5159cc66bb436db93d2cc46dca18ed65c59cc8e4e/fvcore-0.1.5.post20221221.tar.gz#sha256=f2fb0bb90572ae651c11c78e20493ed19b2240550a7e4bbb2d6de87bdd037860\n",
      "Best match: fvcore 0.1.5.post20221221\n",
      "Processing fvcore-0.1.5.post20221221.tar.gz\n",
      "Writing /tmp/easy_install-sti0bx9f/fvcore-0.1.5.post20221221/setup.cfg\n",
      "Running fvcore-0.1.5.post20221221/setup.py -q bdist_egg --dist-dir /tmp/easy_install-sti0bx9f/fvcore-0.1.5.post20221221/egg-dist-tmp-z1dddbv4\n",
      "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n",
      "!!\n",
      "\n",
      "        ********************************************************************************\n",
      "        Please avoid running ``setup.py`` directly.\n",
      "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
      "        other standards-based tools.\n",
      "\n",
      "        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n",
      "        ********************************************************************************\n",
      "\n",
      "!!\n",
      "  self.initialize_options()\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "Adding fvcore 0.1.5.post20221221 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/fvcore-0.1.5.post20221221-py3.10.egg\n",
      "Searching for yacs>=0.1.8\n",
      "Reading https://pypi.org/simple/yacs/\n",
      "Downloading https://files.pythonhosted.org/packages/38/4f/fe9a4d472aa867878ce3bb7efb16654c5d63672b86dc0e6e953a67018433/yacs-0.1.8-py3-none-any.whl#sha256=99f893e30497a4b66842821bac316386f7bd5c4f47ad35c9073ef089aa33af32\n",
      "Best match: yacs 0.1.8\n",
      "Processing yacs-0.1.8-py3-none-any.whl\n",
      "Installing yacs-0.1.8-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding yacs 0.1.8 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/yacs-0.1.8-py3.10.egg\n",
      "Searching for pathspec>=0.9.0\n",
      "Reading https://pypi.org/simple/pathspec/\n",
      "Downloading https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl#sha256=a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08\n",
      "Best match: pathspec 0.12.1\n",
      "Processing pathspec-0.12.1-py3-none-any.whl\n",
      "Installing pathspec-0.12.1-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding pathspec 0.12.1 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/pathspec-0.12.1-py3.10.egg\n",
      "Searching for mypy-extensions>=0.4.3\n",
      "Reading https://pypi.org/simple/mypy-extensions/\n",
      "Downloading https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl#sha256=4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d\n",
      "Best match: mypy-extensions 1.0.0\n",
      "Processing mypy_extensions-1.0.0-py3-none-any.whl\n",
      "Installing mypy_extensions-1.0.0-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding mypy-extensions 1.0.0 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/mypy_extensions-1.0.0-py3.10.egg\n",
      "Searching for antlr4-python3-runtime==4.9.*\n",
      "Reading https://pypi.org/simple/antlr4-python3-runtime/\n",
      "Downloading https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz#sha256=f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b\n",
      "Best match: antlr4-python3-runtime 4.9.3\n",
      "Processing antlr4-python3-runtime-4.9.3.tar.gz\n",
      "Writing /tmp/easy_install-o66d9z0s/antlr4-python3-runtime-4.9.3/setup.cfg\n",
      "Running antlr4-python3-runtime-4.9.3/setup.py -q bdist_egg --dist-dir /tmp/easy_install-o66d9z0s/antlr4-python3-runtime-4.9.3/egg-dist-tmp-czc7y26s\n",
      "warning: no files found matching '*.py' under directory 'test'\n",
      "warning: no files found matching '*.c' under directory 'test'\n",
      "/usr/local/lib/python3.10/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n",
      "!!\n",
      "\n",
      "        ********************************************************************************\n",
      "        Please avoid running ``setup.py`` directly.\n",
      "        Instead, use pypa/build, pypa/installer, pypa/build or\n",
      "        other standards-based tools.\n",
      "\n",
      "        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n",
      "        ********************************************************************************\n",
      "\n",
      "!!\n",
      "  self.initialize_options()\n",
      "zip_safe flag not set; analyzing archive contents...\n",
      "Adding antlr4-python3-runtime 4.9.3 to easy-install.pth file\n",
      "Installing pygrun script to /usr/local/bin\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/antlr4_python3_runtime-4.9.3-py3.10.egg\n",
      "Searching for portalocker\n",
      "Reading https://pypi.org/simple/portalocker/\n",
      "Downloading https://files.pythonhosted.org/packages/17/9e/87671efcca80ba6203811540ed1f9c0462c1609d2281d7b7f53cef05da3d/portalocker-2.8.2-py3-none-any.whl#sha256=cfb86acc09b9aa7c3b43594e19be1345b9d16af3feb08bf92f23d4dce513a28e\n",
      "Best match: portalocker 2.8.2\n",
      "Processing portalocker-2.8.2-py3-none-any.whl\n",
      "Installing portalocker-2.8.2-py3-none-any.whl to /usr/local/lib/python3.10/dist-packages\n",
      "Adding portalocker 2.8.2 to easy-install.pth file\n",
      "\n",
      "Installed /usr/local/lib/python3.10/dist-packages/portalocker-2.8.2-py3.10.egg\n",
      "Searching for packaging==24.0\n",
      "Best match: packaging 24.0\n",
      "Adding packaging 24.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for tensorboard==2.15.2\n",
      "Best match: tensorboard 2.15.2\n",
      "Adding tensorboard 2.15.2 to easy-install.pth file\n",
      "Installing tensorboard script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for tqdm==4.66.4\n",
      "Best match: tqdm 4.66.4\n",
      "Adding tqdm 4.66.4 to easy-install.pth file\n",
      "Installing tqdm script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for cloudpickle==2.2.1\n",
      "Best match: cloudpickle 2.2.1\n",
      "Adding cloudpickle 2.2.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for tabulate==0.9.0\n",
      "Best match: tabulate 0.9.0\n",
      "Adding tabulate 0.9.0 to easy-install.pth file\n",
      "Installing tabulate script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for termcolor==2.4.0\n",
      "Best match: termcolor 2.4.0\n",
      "Adding termcolor 2.4.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for pycocotools==2.0.7\n",
      "Best match: pycocotools 2.0.7\n",
      "Adding pycocotools 2.0.7 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for matplotlib==3.7.1\n",
      "Best match: matplotlib 3.7.1\n",
      "Adding matplotlib 3.7.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for Pillow==9.4.0\n",
      "Best match: Pillow 9.4.0\n",
      "Adding Pillow 9.4.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for typing-extensions==4.12.1\n",
      "Best match: typing-extensions 4.12.1\n",
      "Adding typing-extensions 4.12.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for tomli==2.0.1\n",
      "Best match: tomli 2.0.1\n",
      "Adding tomli 2.0.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for platformdirs==4.2.2\n",
      "Best match: platformdirs 4.2.2\n",
      "Adding platformdirs 4.2.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for click==8.1.7\n",
      "Best match: click 8.1.7\n",
      "Adding click 8.1.7 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for PyYAML==6.0.1\n",
      "Best match: PyYAML 6.0.1\n",
      "Adding PyYAML 6.0.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for numpy==1.25.2\n",
      "Best match: numpy 1.25.2\n",
      "Adding numpy 1.25.2 to easy-install.pth file\n",
      "Installing f2py script to /usr/local/bin\n",
      "Installing f2py3 script to /usr/local/bin\n",
      "Installing f2py3.10 script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for werkzeug==3.0.3\n",
      "Best match: werkzeug 3.0.3\n",
      "Adding werkzeug 3.0.3 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for tensorboard-data-server==0.7.2\n",
      "Best match: tensorboard-data-server 0.7.2\n",
      "Adding tensorboard-data-server 0.7.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for six==1.16.0\n",
      "Best match: six 1.16.0\n",
      "Adding six 1.16.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for setuptools==67.7.2\n",
      "Best match: setuptools 67.7.2\n",
      "Adding setuptools 67.7.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for requests==2.31.0\n",
      "Best match: requests 2.31.0\n",
      "Adding requests 2.31.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for protobuf==3.20.3\n",
      "Best match: protobuf 3.20.3\n",
      "Adding protobuf 3.20.3 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for Markdown==3.6\n",
      "Best match: Markdown 3.6\n",
      "Adding Markdown 3.6 to easy-install.pth file\n",
      "Installing markdown_py script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for google-auth-oauthlib==1.2.0\n",
      "Best match: google-auth-oauthlib 1.2.0\n",
      "Adding google-auth-oauthlib 1.2.0 to easy-install.pth file\n",
      "Installing google-oauthlib-tool script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for google-auth==2.27.0\n",
      "Best match: google-auth 2.27.0\n",
      "Adding google-auth 2.27.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for grpcio==1.64.1\n",
      "Best match: grpcio 1.64.1\n",
      "Adding grpcio 1.64.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for absl-py==1.4.0\n",
      "Best match: absl-py 1.4.0\n",
      "Adding absl-py 1.4.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for python-dateutil==2.8.2\n",
      "Best match: python-dateutil 2.8.2\n",
      "Adding python-dateutil 2.8.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for pyparsing==3.1.2\n",
      "Best match: pyparsing 3.1.2\n",
      "Adding pyparsing 3.1.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for kiwisolver==1.4.5\n",
      "Best match: kiwisolver 1.4.5\n",
      "Adding kiwisolver 1.4.5 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for fonttools==4.53.0\n",
      "Best match: fonttools 4.53.0\n",
      "Adding fonttools 4.53.0 to easy-install.pth file\n",
      "Installing fonttools script to /usr/local/bin\n",
      "Installing pyftmerge script to /usr/local/bin\n",
      "Installing pyftsubset script to /usr/local/bin\n",
      "Installing ttx script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for cycler==0.12.1\n",
      "Best match: cycler 0.12.1\n",
      "Adding cycler 0.12.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for contourpy==1.2.1\n",
      "Best match: contourpy 1.2.1\n",
      "Adding contourpy 1.2.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for MarkupSafe==2.1.5\n",
      "Best match: MarkupSafe 2.1.5\n",
      "Adding MarkupSafe 2.1.5 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for certifi==2024.6.2\n",
      "Best match: certifi 2024.6.2\n",
      "Adding certifi 2024.6.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for urllib3==2.0.7\n",
      "Best match: urllib3 2.0.7\n",
      "Adding urllib3 2.0.7 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for idna==3.7\n",
      "Best match: idna 3.7\n",
      "Adding idna 3.7 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for charset-normalizer==3.3.2\n",
      "Best match: charset-normalizer 3.3.2\n",
      "Adding charset-normalizer 3.3.2 to easy-install.pth file\n",
      "Installing normalizer script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for requests-oauthlib==1.3.1\n",
      "Best match: requests-oauthlib 1.3.1\n",
      "Adding requests-oauthlib 1.3.1 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for rsa==4.9\n",
      "Best match: rsa 4.9\n",
      "Adding rsa 4.9 to easy-install.pth file\n",
      "Installing pyrsa-decrypt script to /usr/local/bin\n",
      "Installing pyrsa-encrypt script to /usr/local/bin\n",
      "Installing pyrsa-keygen script to /usr/local/bin\n",
      "Installing pyrsa-priv2pub script to /usr/local/bin\n",
      "Installing pyrsa-sign script to /usr/local/bin\n",
      "Installing pyrsa-verify script to /usr/local/bin\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for pyasn1-modules==0.4.0\n",
      "Best match: pyasn1-modules 0.4.0\n",
      "Adding pyasn1-modules 0.4.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for cachetools==5.3.3\n",
      "Best match: cachetools 5.3.3\n",
      "Adding cachetools 5.3.3 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for oauthlib==3.2.2\n",
      "Best match: oauthlib 3.2.2\n",
      "Adding oauthlib 3.2.2 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Searching for pyasn1==0.6.0\n",
      "Best match: pyasn1 0.6.0\n",
      "Adding pyasn1 0.6.0 to easy-install.pth file\n",
      "\n",
      "Using /usr/local/lib/python3.10/dist-packages\n",
      "Finished processing dependencies for detectron2==0.6\n",
      "Collecting git+https://github.com/facebookresearch/fvcore.git\n",
      "  Cloning https://github.com/facebookresearch/fvcore.git to /tmp/pip-req-build-3_n6w2t_\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/fvcore.git /tmp/pip-req-build-3_n6w2t_\n",
      "  Resolved https://github.com/facebookresearch/fvcore.git to commit 1d61132af7413155fedc197f72903d01c624bd01\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fvcore==0.1.6) (1.25.2)\n",
      "Requirement already satisfied: yacs>=0.1.6 in /usr/local/lib/python3.10/dist-packages/yacs-0.1.8-py3.10.egg (from fvcore==0.1.6) (0.1.8)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from fvcore==0.1.6) (6.0.1)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from fvcore==0.1.6) (4.66.4)\n",
      "Requirement already satisfied: termcolor>=1.1 in /usr/local/lib/python3.10/dist-packages (from fvcore==0.1.6) (2.4.0)\n",
      "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from fvcore==0.1.6) (9.4.0)\n",
      "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from fvcore==0.1.6) (0.9.0)\n",
      "Requirement already satisfied: iopath>=0.1.7 in /usr/local/lib/python3.10/dist-packages/iopath-0.1.9-py3.10.egg (from fvcore==0.1.6) (0.1.9)\n",
      "Requirement already satisfied: portalocker in /usr/local/lib/python3.10/dist-packages/portalocker-2.8.2-py3.10.egg (from iopath>=0.1.7->fvcore==0.1.6) (2.8.2)\n",
      "Building wheels for collected packages: fvcore\n",
      "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for fvcore: filename=fvcore-0.1.6-py3-none-any.whl size=65574 sha256=037e676bcdf32f37dfb938022021996a6146273acc28b82390fd212b3bf21985\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-d0gvvkew/wheels/8f/cb/6a/3b7ac0e01781855ca3d1417ebf9e15e20d5b7fe37ab063aa50\n",
      "Successfully built fvcore\n",
      "Installing collected packages: fvcore\n",
      "  Attempting uninstall: fvcore\n",
      "    Found existing installation: fvcore 0.1.5.post20221221\n",
      "    Uninstalling fvcore-0.1.5.post20221221:\n",
      "      Successfully uninstalled fvcore-0.1.5.post20221221\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "detectron2 0.6 requires fvcore<0.1.6,>=0.1.5, but you have fvcore 0.1.6 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed fvcore-0.1.6\n",
      "/content\n",
      "Collecting torch_snippets\n",
      "  Downloading torch_snippets-0.533-py3-none-any.whl (90 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.3/90.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "Collecting loguru (from torch_snippets)\n",
      "  Downloading loguru-0.7.2-py3-none-any.whl (62 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "Collecting typing (from torch_snippets)\n",
      "  Downloading typing-3.7.4.3.tar.gz (78 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: srsly in /usr/local/lib/python3.10/dist-packages (from torch_snippets) (2.4.8)\n",
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      "Collecting jsonlines (from torch_snippets)\n",
      "  Downloading jsonlines-4.0.0-py3-none-any.whl (8.7 kB)\n",
      "Requirement already satisfied: imgaug>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from torch_snippets) (0.4.0)\n",
      "Collecting xmltodict (from torch_snippets)\n",
      "  Downloading xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
      "Collecting fuzzywuzzy (from torch_snippets)\n",
      "  Downloading fuzzywuzzy-0.18.0-py2.py3-none-any.whl (18 kB)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from torch_snippets) (1.2.2)\n",
      "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from torch_snippets) (3.8.1)\n",
      "Collecting python-Levenshtein (from torch_snippets)\n",
      "  Downloading python_Levenshtein-0.25.1-py3-none-any.whl (9.4 kB)\n",
      "Collecting pre-commit (from torch_snippets)\n",
      "  Downloading pre_commit-3.7.1-py2.py3-none-any.whl (204 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m204.3/204.3 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting pymupdf (from torch_snippets)\n",
      "  Downloading PyMuPDF-1.24.5-cp310-none-manylinux2014_x86_64.whl (3.5 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: nbconvert in /usr/local/lib/python3.10/dist-packages (from torch_snippets) (6.5.4)\n",
      "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (from torch_snippets) (5.10.4)\n",
      "Collecting icecream (from torch_snippets)\n",
      "  Downloading icecream-2.1.3-py2.py3-none-any.whl (8.4 kB)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from imgaug>=0.4.0->torch_snippets) (1.16.0)\n",
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      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from altair->torch_snippets) (3.1.4)\n",
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      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from fastcore->torch_snippets) (24.0)\n",
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      "Building wheels for collected packages: typing\n",
      "  Building wheel for typing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for typing: filename=typing-3.7.4.3-py3-none-any.whl size=26306 sha256=238ffd73e025a80e39bfc3b9e6b7a4c19c7d353c109334c4bcacdf860d7d7606\n",
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      "Successfully built typing\n",
      "Installing collected packages: fuzzywuzzy, distlib, xmltodict, virtualenv, typing, rapidfuzz, PyMuPDFb, nodeenv, loguru, jsonlines, jedi, identify, executing, dill, colorama, cfgv, asttokens, pymupdf, pre-commit, Levenshtein, icecream, python-Levenshtein, torch_snippets\n",
      "Successfully installed Levenshtein-0.25.1 PyMuPDFb-1.24.3 asttokens-2.4.1 cfgv-3.4.0 colorama-0.4.6 dill-0.3.8 distlib-0.3.8 executing-2.0.1 fuzzywuzzy-0.18.0 icecream-2.1.3 identify-2.5.36 jedi-0.19.1 jsonlines-4.0.0 loguru-0.7.2 nodeenv-1.9.1 pre-commit-3.7.1 pymupdf-1.24.5 python-Levenshtein-0.25.1 rapidfuzz-3.9.3 torch_snippets-0.533 typing-3.7.4.3 virtualenv-20.26.2 xmltodict-0.13.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "id": "1f027c08768b45f1a1d1ae00c9e232a7",
       "pip_warning": {
        "packages": [
         "typing"
        ]
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
      "  \n",
      "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
      "  \u001b[31m╰─>\u001b[0m See above for output.\n",
      "  \n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
      "\n",
      "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
      "\u001b[31m╰─>\u001b[0m See above for output.\n",
      "\n",
      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
      "\u001b[1;36mhint\u001b[0m: See above for details.\n"
     ]
    }
   ],
   "source": [
    "%cd /content/\n",
    "# install detectron2:\n",
    "!git clone https://github.com/facebookresearch/detectron2\n",
    "%cd /content/detectron2\n",
    "%pip install -r requirements.txt\n",
    "!python setup.py install\n",
    "%pip install git+https://github.com/facebookresearch/fvcore.git\n",
    "%cd /content/\n",
    "\n",
    "%pip install torch_snippets\n",
    "%pip install pyyaml==5.1 pycocotools>=2.0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_LSpPfd_fEOd"
   },
   "source": [
    "Restart your notebook after running above cell.  \n",
    "Once restarted, you can directly start running below cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "FQgfG1e_fEOd"
   },
   "outputs": [],
   "source": [
    "from torch_snippets import *\n",
    "import detectron2\n",
    "from detectron2.utils.logger import setup_logger\n",
    "setup_logger()\n",
    "\n",
    "from detectron2 import model_zoo\n",
    "from detectron2.engine import DefaultPredictor\n",
    "from detectron2.config import get_cfg\n",
    "from detectron2.utils.visualizer import Visualizer\n",
    "from detectron2.data import MetadataCatalog, DatasetCatalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Joo_heUFch-4"
   },
   "outputs": [],
   "source": [
    "cfg = get_cfg() # get a fresh new config\n",
    "cfg.merge_from_file(model_zoo.get_config_file(\"COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eBBgf18kcsfR",
    "outputId": "5c09a74a-ff54-40c0-ed02-ab0dc47b606f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[06/08 15:43:49 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl ...\n"
     ]
    }
   ],
   "source": [
    "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model\n",
    "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml\")\n",
    "predictor = DefaultPredictor(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "wxteXWiOcvYe"
   },
   "outputs": [],
   "source": [
    "from torch_snippets import read, resize\n",
    "!wget -q https://upload.wikimedia.org/wikipedia/commons/e/ef/Bydgoszcz_2016_IAAF_World_U20_Championships%2C_200m_women_semi-final3_22-07-2016.jpg -O image.png\n",
    "im = read('image.png',1)\n",
    "im = resize(im, 0.5) # resize image to half its dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "id": "Ax7fY1fmcygb",
    "outputId": "cdb72397-e8df-46a7-97c4-12209ccb9ff9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x794d0c936470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outputs = predictor(im)\n",
    "v = Visualizer(im[:,:,::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)\n",
    "out = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(out.get_image())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R2PHSHCOc3RT"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Human_pose_detection (1).ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
